ArticleSensors (Basel, Switzerland)2021
ECG Authentication Based on Non-Linear Normalization under Various Physiological Conditions.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A PRISMA-based systematic review on advances in identity recognition and authentication using human biometric signals (2018-2023).Biomedical engineering online · 2026Pooled it
- ECG-Based Biometric Recognition: A Survey of Methods and Databases.Sensors (Basel, Switzerland) · 2025Review
- A Resting ECG Screening Protocol Improved with Artificial Intelligence for the Early Detection of Cardiovascular Risk in Athletes.Diagnostics (Basel, Switzerland) · 2025Article
- Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features.Sensors (Basel, Switzerland) · 2024Article
- Person identification with arrhythmic ECG signals using deep convolution neural network.Scientific reports · 2024Article
- Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2023Article
- Initial Study Using Electrocardiogram for Authentication and Identification.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
The development and use of wearable devices require high levels of security and have sparked interest in biometric authentication research. Among the available approaches, electrocardiogram (ECG) technology is attracting attention because of its strengths in spoofing. However, morphological changes of ECG, which are affected by physical and psychological factors, can make authentication difficult. In this paper, we propose authentication using non-linear normalization of ECG beats that is robust to changes in ECG waveforms according to heart rate fluctuations in various daily activities. We performed a non-linear normalization method through the analysis of ECG alongside heart rate, evaluating similarities and authenticating the performance of our new method compared to existing methods. Compared with beats before normalization, the average similarity of the proposed method increased 23.7% in the resting state and 43% in the non-resting state. After learning in the resting state, authentication performance reached 99.05% accuracy for the resting state and 88.14% for the non-resting state. The proposed method can be applicable to an ECG-based authentication system under various physiological conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.